Rendering is the process of generating 2D images from 3D assets, simulated in
a virtual environment, typically with a graphics pipeline. By inverting such
renderer, one can think of a learning approach to predict a 3D shape from an
input image. However, standard rendering pipelines involve a fundamental
discretization step called rasterization, which prevents the rendering process
to be differentiable, hence suitable for learning. We present the first
non-parametric and truly differentiable rasterizer based on silhouettes. Our
method enables unsupervised learning for high-quality 3D mesh reconstruction
from a single image. We call our framework `soft rasterizer' as it provides an
accurate soft approximation of the standard rasterizer. The key idea is to fuse
the probabilistic contributions of all mesh triangles with respect to the
rendered pixels. When combined with a mesh generator in a deep neural network,
our soft rasterizer is able to generate an approximated silhouette of the
generated polygon mesh in the forward pass. The rendering loss is
back-propagated to supervise the mesh generation without the need of 3D
training data. Experimental results demonstrate that our approach significantly
outperforms the state-of-the-art unsupervised techniques, both quantitatively
and qualitatively. We also show that our soft rasterizer can achieve comparable
results to the cutting-edge supervised learning method and in various cases
even better ones, especially for real-world data.

本文提出了一种基于 contour 的非参数、真正可微分的 soft rasterizer，使得可以从单张图片进行高质量 3D 网格重建的无监督学习成为可能，且相比其他无监督方法表现出了极大的优势。同时，我们还展示了在各种实际情况下，我们的 soft rasterizer 能够获得与最前沿的有监督学习方法可比甚至更好的结果。